Machine Learning

Lityx, LLC announces the release of LityxIQ 6.0

Lityx makes its leading alternative AI and MLOps platform easier to deliver value for organizations focused on digital transformation

Lityx, LLC today announced the release of LityxIQ 6.0, the first AutoML platform to combine machine learning with mathematical optimization in a single, cloud-hosted, no-code platform. A fully integrated enterprise decision engine, LityxIQ 6.0 extends a proven track record of success delivering rapid predictive and prescriptive insights, and simplifies model development, management, deployment, and monitoring to genuinely democratize advanced analytics for organizations of any size.

“Lityx combines a guided Customer Success strategy with our best-in-class LityxIQ platform to get analytics capabilities in the hands of anyone who uses data insights to make critical business decisions,” said Paul Maiste, Ph.D., Lityx CEO and president. “LityxIQ is built by data scientists for analysts and statisticians, alike, to accelerate advanced analytics success to days or weeks versus months or years. Plus, LityxIQ provides immediate value to business leaders by making insights easy to understand for arriving at the best decisions faster, at a price to meet any organization’s budget.”

LityxIQ 6.0 users get enhanced MLOps functionality that streamlines machine learning development and production, ensuring that models remain robust, reliable and scalable. Additionally, through available solution accelerators, LityxIQ 6.0 makes the path from data to insights even faster.

“The platform has included essential tools for managing the end-to-end data lifecycle since our launch, and LityxIQ 6.0 makes decision intelligence even easier through additional data automation and a refreshed interface for a world-class user experience,” said Dr. Maiste.

Industries achieving success through LityxIQ include global manufacturers, healthcare, financial services, media and advertising agencies, and more.

Notable enhancements in LityxIQ 6.0 include automated model monitoring, enhanced model performance analysis and comparisons, and additional model exploration tools such as customer engagement profitability optimization and threshold and cost optimization.

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